Overview

Dataset statistics

Number of variables15
Number of observations1119
Missing cells4600
Missing cells (%)27.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory131.3 KiB
Average record size in memory120.1 B

Variable types

Categorical4
Numeric10
Unsupported1

Alerts

_id has a high cardinality: 1119 distinct valuesHigh cardinality
userId has a high cardinality: 258 distinct valuesHigh cardinality
bonusPointsEarned is highly overall correlated with bonusPointsEarnedReasonHigh correlation
createDate is highly overall correlated with dateScanned and 4 other fieldsHigh correlation
dateScanned is highly overall correlated with createDate and 4 other fieldsHigh correlation
finishedDate is highly overall correlated with createDate and 5 other fieldsHigh correlation
modifyDate is highly overall correlated with createDate and 4 other fieldsHigh correlation
pointsAwardedDate is highly overall correlated with createDate and 5 other fieldsHigh correlation
pointsEarned is highly overall correlated with bonusPointsEarnedReason and 1 other fieldsHigh correlation
purchaseDate is highly overall correlated with createDate and 5 other fieldsHigh correlation
purchasedItemCount is highly overall correlated with totalSpentHigh correlation
totalSpent is highly overall correlated with purchasedItemCount and 1 other fieldsHigh correlation
bonusPointsEarnedReason is highly overall correlated with bonusPointsEarned and 2 other fieldsHigh correlation
rewardsReceiptStatus is highly overall correlated with finishedDate and 3 other fieldsHigh correlation
bonusPointsEarned has 575 (51.4%) missing valuesMissing
bonusPointsEarnedReason has 575 (51.4%) missing valuesMissing
finishedDate has 551 (49.2%) missing valuesMissing
pointsAwardedDate has 582 (52.0%) missing valuesMissing
pointsEarned has 510 (45.6%) missing valuesMissing
purchaseDate has 448 (40.0%) missing valuesMissing
purchasedItemCount has 484 (43.3%) missing valuesMissing
rewardsReceiptItemList has 440 (39.3%) missing valuesMissing
totalSpent has 435 (38.9%) missing valuesMissing
_id is uniformly distributedUniform
_id has unique valuesUnique
rewardsReceiptItemList is an unsupported type, check if it needs cleaning or further analysisUnsupported
purchasedItemCount has 15 (1.3%) zerosZeros
totalSpent has 15 (1.3%) zerosZeros

Reproduction

Analysis started2023-07-19 20:07:42.611522
Analysis finished2023-07-19 20:08:12.922463
Duration30.31 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
5ff1e1eb0a720f0523000575
 
1
601b48df0a720f05f4000238
 
1
601c11410a720f05f400028e
 
1
601c2d630a7214ad280002a7
 
1
601be7120a720f05f400027a
 
1
Other values (1114)
1114 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters26856
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1119 ?
Unique (%)100.0%

Sample

1st row5ff1e1eb0a720f0523000575
2nd row5ff1e1bb0a720f052300056b
3rd row5ff1e1f10a720f052300057a
4th row5ff1e1ee0a7214ada100056f
5th row5ff1e1d20a7214ada1000561

Common Values

ValueCountFrequency (%)
5ff1e1eb0a720f0523000575 1
 
0.1%
601b48df0a720f05f4000238 1
 
0.1%
601c11410a720f05f400028e 1
 
0.1%
601c2d630a7214ad280002a7 1
 
0.1%
601be7120a720f05f400027a 1
 
0.1%
601b5cc90a7214ad2800023a 1
 
0.1%
601bc2bd0a7214ad28000262 1
 
0.1%
601c2c3a0a720f05f40002a2 1
 
0.1%
601b730f0a7214ad28000243 1
 
0.1%
601bd57e0a7214ad2800026d 1
 
0.1%
Other values (1109) 1109
99.1%

Length

2023-07-19T13:08:13.011993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5ff1e1eb0a720f0523000575 1
 
0.1%
5ff36d860a720f05230005a7 1
 
0.1%
5ff1e1f10a720f052300057a 1
 
0.1%
5ff1e1ee0a7214ada100056f 1
 
0.1%
5ff1e1d20a7214ada1000561 1
 
0.1%
5ff1e1e40a7214ada1000566 1
 
0.1%
5ff1e1cd0a720f052300056f 1
 
0.1%
5ff1e1a40a720f0523000569 1
 
0.1%
5ff1e1ed0a7214ada100056e 1
 
0.1%
5ff473a90a7214ada10005c2 1
 
0.1%
Other values (1109) 1109
99.1%

Most occurring characters

ValueCountFrequency (%)
0 7683
28.6%
a 2271
 
8.5%
2 2212
 
8.2%
f 1789
 
6.7%
7 1774
 
6.6%
1 1759
 
6.5%
5 1614
 
6.0%
6 1373
 
5.1%
4 1177
 
4.4%
d 1030
 
3.8%
Other values (6) 4174
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19948
74.3%
Lowercase Letter 6908
 
25.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7683
38.5%
2 2212
 
11.1%
7 1774
 
8.9%
1 1759
 
8.8%
5 1614
 
8.1%
6 1373
 
6.9%
4 1177
 
5.9%
8 898
 
4.5%
3 885
 
4.4%
9 573
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
a 2271
32.9%
f 1789
25.9%
d 1030
14.9%
c 731
 
10.6%
e 610
 
8.8%
b 477
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 19948
74.3%
Latin 6908
 
25.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7683
38.5%
2 2212
 
11.1%
7 1774
 
8.9%
1 1759
 
8.8%
5 1614
 
8.1%
6 1373
 
6.9%
4 1177
 
5.9%
8 898
 
4.5%
3 885
 
4.4%
9 573
 
2.9%
Latin
ValueCountFrequency (%)
a 2271
32.9%
f 1789
25.9%
d 1030
14.9%
c 731
 
10.6%
e 610
 
8.8%
b 477
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7683
28.6%
a 2271
 
8.5%
2 2212
 
8.2%
f 1789
 
6.7%
7 1774
 
6.6%
1 1759
 
6.5%
5 1614
 
6.0%
6 1373
 
5.1%
4 1177
 
4.4%
d 1030
 
3.8%
Other values (6) 4174
15.5%

bonusPointsEarned
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)2.2%
Missing575
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean238.89338
Minimum5
Maximum750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:13.113917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median45
Q3500
95-th percentile750
Maximum750
Range745
Interquartile range (IQR)495

Descriptive statistics

Standard deviation299.09173
Coefficient of variation (CV)1.2519883
Kurtosis-0.91662581
Mean238.89338
Median Absolute Deviation (MAD)40
Skewness0.89974537
Sum129958
Variance89455.863
MonotonicityNot monotonic
2023-07-19T13:08:13.215163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 183
 
16.4%
750 119
 
10.6%
25 71
 
6.3%
250 31
 
2.8%
45 31
 
2.8%
500 30
 
2.7%
150 27
 
2.4%
300 26
 
2.3%
100 18
 
1.6%
27 6
 
0.5%
Other values (2) 2
 
0.2%
(Missing) 575
51.4%
ValueCountFrequency (%)
5 183
16.4%
21 1
 
0.1%
25 71
 
6.3%
27 6
 
0.5%
40 1
 
0.1%
45 31
 
2.8%
100 18
 
1.6%
150 27
 
2.4%
250 31
 
2.8%
300 26
 
2.3%
ValueCountFrequency (%)
750 119
10.6%
500 30
 
2.7%
300 26
 
2.3%
250 31
 
2.8%
150 27
 
2.4%
100 18
 
1.6%
45 31
 
2.8%
40 1
 
0.1%
27 6
 
0.5%
25 71
6.3%

bonusPointsEarnedReason
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)1.7%
Missing575
Missing (%)51.4%
Memory size8.9 KiB
All-receipts receipt bonus
183 
Receipt number 1 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)
119 
COMPLETE_NONPARTNER_RECEIPT
71 
COMPLETE_PARTNER_RECEIPT
39 
Receipt number 3 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)
31 
Other values (4)
101 

Length

Max length83
Median length27
Mean length52.286765
Min length24

Characters and Unicode

Total characters28444
Distinct characters46
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReceipt number 2 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)
2nd rowReceipt number 5 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)
3rd rowAll-receipts receipt bonus
4th rowAll-receipts receipt bonus
5th rowAll-receipts receipt bonus

Common Values

ValueCountFrequency (%)
All-receipts receipt bonus 183
 
16.4%
Receipt number 1 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36) 119
 
10.6%
COMPLETE_NONPARTNER_RECEIPT 71
 
6.3%
COMPLETE_PARTNER_RECEIPT 39
 
3.5%
Receipt number 3 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36) 31
 
2.8%
Receipt number 2 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36) 30
 
2.7%
Receipt number 5 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36) 27
 
2.4%
Receipt number 4 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36) 26
 
2.3%
Receipt number 6 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36) 18
 
1.6%
(Missing) 575
51.4%

Length

2023-07-19T13:08:13.342089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T13:08:13.495936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
receipt 434
14.9%
bonus 434
14.9%
default 251
8.6%
number 251
8.6%
5cefdcacf3693e0b50e83a36 251
8.6%
completed 251
8.6%
point 251
8.6%
schedule 251
8.6%
all-receipts 183
6.3%
1 119
 
4.1%
Other values (7) 242
8.3%

Most occurring characters

ValueCountFrequency (%)
e 3242
 
11.4%
2374
 
8.3%
c 1872
 
6.6%
p 1119
 
3.9%
t 1119
 
3.9%
3 1035
 
3.6%
n 936
 
3.3%
u 936
 
3.3%
b 936
 
3.3%
o 936
 
3.3%
Other values (36) 13939
49.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16827
59.2%
Uppercase Letter 4824
 
17.0%
Decimal Number 3263
 
11.5%
Space Separator 2374
 
8.3%
Close Punctuation 251
 
0.9%
Open Punctuation 251
 
0.9%
Other Punctuation 251
 
0.9%
Connector Punctuation 220
 
0.8%
Dash Punctuation 183
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3242
19.3%
c 1872
11.1%
p 1119
 
6.7%
t 1119
 
6.7%
n 936
 
5.6%
u 936
 
5.6%
b 936
 
5.6%
o 936
 
5.6%
s 868
 
5.2%
l 868
 
5.2%
Other values (7) 3995
23.7%
Uppercase Letter
ValueCountFrequency (%)
E 801
16.6%
T 581
12.0%
R 581
12.0%
A 544
11.3%
L 361
7.5%
P 330
6.8%
N 252
 
5.2%
F 251
 
5.2%
U 251
 
5.2%
D 251
 
5.2%
Other values (4) 621
12.9%
Decimal Number
ValueCountFrequency (%)
3 1035
31.7%
5 529
16.2%
6 520
15.9%
0 502
15.4%
8 251
 
7.7%
9 251
 
7.7%
1 119
 
3.6%
2 30
 
0.9%
4 26
 
0.8%
Space Separator
ValueCountFrequency (%)
2374
100.0%
Close Punctuation
ValueCountFrequency (%)
) 251
100.0%
Open Punctuation
ValueCountFrequency (%)
( 251
100.0%
Other Punctuation
ValueCountFrequency (%)
, 251
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 220
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21651
76.1%
Common 6793
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3242
 
15.0%
c 1872
 
8.6%
p 1119
 
5.2%
t 1119
 
5.2%
n 936
 
4.3%
u 936
 
4.3%
b 936
 
4.3%
o 936
 
4.3%
s 868
 
4.0%
l 868
 
4.0%
Other values (21) 8819
40.7%
Common
ValueCountFrequency (%)
2374
34.9%
3 1035
15.2%
5 529
 
7.8%
6 520
 
7.7%
0 502
 
7.4%
) 251
 
3.7%
8 251
 
3.7%
9 251
 
3.7%
( 251
 
3.7%
, 251
 
3.7%
Other values (5) 578
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3242
 
11.4%
2374
 
8.3%
c 1872
 
6.6%
p 1119
 
3.9%
t 1119
 
3.9%
3 1035
 
3.6%
n 936
 
3.3%
u 936
 
3.3%
b 936
 
3.3%
o 936
 
3.3%
Other values (36) 13939
49.0%

createDate
Real number (ℝ)

Distinct1107
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6117998 × 1012
Minimum1.6040891 × 1012
Maximum1.6146407 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:13.693929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6040891 × 1012
5-th percentile1.6097889 × 1012
Q11.6106516 × 1012
median1.6119407 × 1012
Q31.612704 × 1012
95-th percentile1.6144754 × 1012
Maximum1.6146407 × 1012
Range1.0551576 × 1010
Interquartile range (IQR)2.05243 × 109

Descriptive statistics

Standard deviation1.484091 × 109
Coefficient of variation (CV)0.00092076632
Kurtosis2.4018826
Mean1.6117998 × 1012
Median Absolute Deviation (MAD)9.3145338 × 108
Skewness-0.47574632
Sum1.803604 × 1015
Variance2.202526 × 1018
MonotonicityNot monotonic
2023-07-19T13:08:13.848530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.609687531 × 10123
 
0.3%
1.610144383 × 10123
 
0.3%
1.609687534 × 10123
 
0.3%
1.609879107 × 10122
 
0.2%
1.610390945 × 10122
 
0.2%
1.609945606 × 10122
 
0.2%
1.60994556 × 10122
 
0.2%
1.611957559 × 10122
 
0.2%
1.612972619 × 10122
 
0.2%
1.612733265 × 10121
 
0.1%
Other values (1097) 1097
98.0%
ValueCountFrequency (%)
1.604089079 × 10121
0.1%
1.604089081 × 10121
0.1%
1.604607818 × 10121
0.1%
1.604693303 × 10121
0.1%
1.604900211 × 10121
0.1%
1.604900217 × 10121
0.1%
1.605483857 × 10121
0.1%
1.605556824 × 10121
0.1%
1.609607165 × 10121
0.1%
1.609687446 × 10121
0.1%
ValueCountFrequency (%)
1.614640655 × 10121
0.1%
1.614634348 × 10121
0.1%
1.614633447 × 10121
0.1%
1.614626853 × 10121
0.1%
1.61462295 × 10121
0.1%
1.614620855 × 10121
0.1%
1.614613362 × 10121
0.1%
1.614607658 × 10121
0.1%
1.614607054 × 10121
0.1%
1.614604048 × 10121
0.1%

dateScanned
Real number (ℝ)

Distinct1107
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6117998 × 1012
Minimum1.6040891 × 1012
Maximum1.6146407 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:14.014449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6040891 × 1012
5-th percentile1.6097889 × 1012
Q11.6106516 × 1012
median1.6119407 × 1012
Q31.612704 × 1012
95-th percentile1.6144754 × 1012
Maximum1.6146407 × 1012
Range1.0551576 × 1010
Interquartile range (IQR)2.05243 × 109

Descriptive statistics

Standard deviation1.484091 × 109
Coefficient of variation (CV)0.00092076632
Kurtosis2.4018826
Mean1.6117998 × 1012
Median Absolute Deviation (MAD)9.3145338 × 108
Skewness-0.47574632
Sum1.803604 × 1015
Variance2.202526 × 1018
MonotonicityNot monotonic
2023-07-19T13:08:14.168571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.609687531 × 10123
 
0.3%
1.610144383 × 10123
 
0.3%
1.609687534 × 10123
 
0.3%
1.609879107 × 10122
 
0.2%
1.610390945 × 10122
 
0.2%
1.609945606 × 10122
 
0.2%
1.60994556 × 10122
 
0.2%
1.611957559 × 10122
 
0.2%
1.612972619 × 10122
 
0.2%
1.612733265 × 10121
 
0.1%
Other values (1097) 1097
98.0%
ValueCountFrequency (%)
1.604089079 × 10121
0.1%
1.604089081 × 10121
0.1%
1.604607818 × 10121
0.1%
1.604693303 × 10121
0.1%
1.604900211 × 10121
0.1%
1.604900217 × 10121
0.1%
1.605483857 × 10121
0.1%
1.605556824 × 10121
0.1%
1.609607165 × 10121
0.1%
1.609687446 × 10121
0.1%
ValueCountFrequency (%)
1.614640655 × 10121
0.1%
1.614634348 × 10121
0.1%
1.614633447 × 10121
0.1%
1.614626853 × 10121
0.1%
1.61462295 × 10121
0.1%
1.614620855 × 10121
0.1%
1.614613362 × 10121
0.1%
1.614607658 × 10121
0.1%
1.614607054 × 10121
0.1%
1.614604048 × 10121
0.1%

finishedDate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct553
Distinct (%)97.4%
Missing551
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean1.6110582 × 1012
Minimum1.6096874 × 1012
Maximum1.614379 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:14.328107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6096874 × 1012
5-th percentile1.6097881 × 1012
Q11.610141 × 1012
median1.6110908 × 1012
Q31.6117693 × 1012
95-th percentile1.6125454 × 1012
Maximum1.614379 × 1012
Range4.691535 × 109
Interquartile range (IQR)1.628371 × 109

Descriptive statistics

Standard deviation9.5346414 × 108
Coefficient of variation (CV)0.00059182476
Kurtosis-0.79232439
Mean1.6110582 × 1012
Median Absolute Deviation (MAD)7.605265 × 108
Skewness0.3062952
Sum9.1508106 × 1014
Variance9.0909386 × 1017
MonotonicityNot monotonic
2023-07-19T13:08:14.477145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.609687534 × 10123
 
0.3%
1.609687531 × 10122
 
0.2%
1.609687532 × 10122
 
0.2%
1.611641366 × 10122
 
0.2%
1.609788557 × 10122
 
0.2%
1.609788876 × 10122
 
0.2%
1.611173216 × 10122
 
0.2%
1.61038737 × 10122
 
0.2%
1.611172426 × 10122
 
0.2%
1.610144383 × 10122
 
0.2%
Other values (543) 547
48.9%
(Missing) 551
49.2%
ValueCountFrequency (%)
1.60968745 × 10121
0.1%
1.609687461 × 10121
0.1%
1.609687475 × 10121
0.1%
1.609687483 × 10121
0.1%
1.609687493 × 10121
0.1%
1.609687499 × 10121
0.1%
1.609687502 × 10122
0.2%
1.609687509 × 10121
0.1%
1.609687511 × 10121
0.1%
1.609687512 × 10121
0.1%
ValueCountFrequency (%)
1.614378985 × 10121
0.1%
1.61313912 × 10121
0.1%
1.613139118 × 10121
0.1%
1.613139071 × 10121
0.1%
1.613139065 × 10121
0.1%
1.613060278 × 10121
0.1%
1.613060276 × 10121
0.1%
1.613060271 × 10121
0.1%
1.613052084 × 10121
0.1%
1.613052054 × 10121
0.1%

modifyDate
Real number (ℝ)

Distinct1104
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6118469 × 1012
Minimum1.6096874 × 1012
Maximum1.6146407 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:14.620801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6096874 × 1012
5-th percentile1.6097897 × 1012
Q11.6106599 × 1012
median1.6119407 × 1012
Q31.612704 × 1012
95-th percentile1.6144754 × 1012
Maximum1.6146407 × 1012
Range4.9532048 × 109
Interquartile range (IQR)2.0440682 × 109

Descriptive statistics

Standard deviation1.3615765 × 109
Coefficient of variation (CV)0.00084473068
Kurtosis-0.6315802
Mean1.6118469 × 1012
Median Absolute Deviation (MAD)9.128301 × 108
Skewness0.27102152
Sum1.8036566 × 1015
Variance1.8538906 × 1018
MonotonicityNot monotonic
2023-07-19T13:08:14.777817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.609687536 × 10123
 
0.3%
1.610144388 × 10122
 
0.2%
1.609855952 × 10122
 
0.2%
1.61038737 × 10122
 
0.2%
1.61039095 × 10122
 
0.2%
1.611641366 × 10122
 
0.2%
1.612225697 × 10122
 
0.2%
1.612225704 × 10122
 
0.2%
1.612225662 × 10122
 
0.2%
1.609788876 × 10122
 
0.2%
Other values (1094) 1098
98.1%
ValueCountFrequency (%)
1.60968745 × 10121
0.1%
1.609687461 × 10121
0.1%
1.609687475 × 10121
0.1%
1.609687477 × 10121
0.1%
1.609687478 × 10121
0.1%
1.609687488 × 10121
0.1%
1.609687493 × 10121
0.1%
1.609687494 × 10121
0.1%
1.609687499 × 10121
0.1%
1.609687502 × 10122
0.2%
ValueCountFrequency (%)
1.614640655 × 10121
0.1%
1.614634349 × 10121
0.1%
1.614633448 × 10121
0.1%
1.614626854 × 10121
0.1%
1.614622951 × 10121
0.1%
1.614620855 × 10121
0.1%
1.614613362 × 10121
0.1%
1.614607658 × 10121
0.1%
1.614607054 × 10121
0.1%
1.614604049 × 10121
0.1%

pointsAwardedDate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct523
Distinct (%)97.4%
Missing582
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean1.6109486 × 1012
Minimum1.6040891 × 1012
Maximum1.614379 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:14.935338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6040891 × 1012
5-th percentile1.6096875 × 1012
Q11.6100433 × 1012
median1.6110896 × 1012
Q31.6117099 × 1012
95-th percentile1.6124746 × 1012
Maximum1.614379 × 1012
Range1.0289905 × 1010
Interquartile range (IQR)1.666597 × 109

Descriptive statistics

Standard deviation1.0567179 × 109
Coefficient of variation (CV)0.00065596004
Kurtosis6.4349848
Mean1.6109486 × 1012
Median Absolute Deviation (MAD)7.61714 × 108
Skewness-1.0478511
Sum8.6507938 × 1014
Variance1.1166527 × 1018
MonotonicityNot monotonic
2023-07-19T13:08:15.106305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.609687534 × 10123
 
0.3%
1.609687531 × 10122
 
0.2%
1.610144383 × 10122
 
0.2%
1.609788557 × 10122
 
0.2%
1.609879104 × 10122
 
0.2%
1.610557181 × 10122
 
0.2%
1.609788876 × 10122
 
0.2%
1.611173216 × 10122
 
0.2%
1.611172426 × 10122
 
0.2%
1.609687532 × 10122
 
0.2%
Other values (513) 516
46.1%
(Missing) 582
52.0%
ValueCountFrequency (%)
1.60408908 × 10121
0.1%
1.604900212 × 10121
0.1%
1.605483857 × 10121
0.1%
1.605556824 × 10121
0.1%
1.60968745 × 10121
0.1%
1.609687459 × 10121
0.1%
1.609687461 × 10121
0.1%
1.609687475 × 10121
0.1%
1.609687476 × 10121
0.1%
1.609687483 × 10121
0.1%
ValueCountFrequency (%)
1.614378985 × 10121
0.1%
1.61313912 × 10121
0.1%
1.613139117 × 10121
0.1%
1.613139071 × 10121
0.1%
1.613139065 × 10121
0.1%
1.613060278 × 10121
0.1%
1.613060276 × 10121
0.1%
1.613060266 × 10121
0.1%
1.613052084 × 10121
0.1%
1.613052054 × 10121
0.1%

pointsEarned
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct119
Distinct (%)19.5%
Missing510
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean585.96289
Minimum0
Maximum10199.8
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:15.519454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median150
Q3750
95-th percentile2035.3
Maximum10199.8
Range10199.8
Interquartile range (IQR)745

Descriptive statistics

Standard deviation1357.1669
Coefficient of variation (CV)2.3161312
Kurtosis25.00351
Mean585.96289
Median Absolute Deviation (MAD)145
Skewness4.7279179
Sum356851.4
Variance1841902.1
MonotonicityNot monotonic
2023-07-19T13:08:15.669662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 161
 
14.4%
25 63
 
5.6%
750 49
 
4.4%
250 37
 
3.3%
50 27
 
2.4%
840 21
 
1.9%
100 18
 
1.6%
500 17
 
1.5%
300 16
 
1.4%
150 15
 
1.3%
Other values (109) 185
 
16.5%
(Missing) 510
45.6%
ValueCountFrequency (%)
0 4
 
0.4%
5 161
14.4%
25 63
 
5.6%
35 7
 
0.6%
50 27
 
2.4%
50.6 5
 
0.4%
50.9 1
 
0.1%
55 6
 
0.5%
91.2 2
 
0.2%
94.6 1
 
0.1%
ValueCountFrequency (%)
10199.8 1
 
0.1%
9850 1
 
0.1%
9449.8 1
 
0.1%
9200 1
 
0.1%
8950 1
 
0.1%
8850 1
 
0.1%
8700 3
0.3%
7137.2 1
 
0.1%
6257.3 1
 
0.1%
5850 2
0.2%

purchaseDate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct358
Distinct (%)53.4%
Missing448
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean1.6085477 × 1012
Minimum1.5093216 × 1012
Maximum1.615225 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:15.820017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5093216 × 1012
5-th percentile1.5976224 × 1012
Q11.6098048 × 1012
median1.6105511 × 1012
Q31.6116236 × 1012
95-th percentile1.6124832 × 1012
Maximum1.615225 × 1012
Range1.0590343 × 1011
Interquartile range (IQR)1.818778 × 109

Descriptive statistics

Standard deviation1.230755 × 1010
Coefficient of variation (CV)0.007651343
Kurtosis55.265028
Mean1.6085477 × 1012
Median Absolute Deviation (MAD)8.48982 × 108
Skewness-7.2846834
Sum1.0793355 × 1015
Variance1.514758 × 1020
MonotonicityNot monotonic
2023-07-19T13:08:15.994621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5976224 × 101240
 
3.6%
1.6124832 × 101228
 
2.5%
1.611792 × 101224
 
2.1%
1.6104096 × 101223
 
2.1%
1.6111008 × 101218
 
1.6%
1.6103232 × 101214
 
1.3%
1.6118784 × 101213
 
1.2%
1.6112736 × 101211
 
1.0%
1.6111872 × 101211
 
1.0%
1.6105824 × 101211
 
1.0%
Other values (348) 478
42.7%
(Missing) 448
40.0%
ValueCountFrequency (%)
1.5093216 × 10129
 
0.8%
1.5464736 × 10121
 
0.1%
1.5976224 × 101240
3.6%
1.5999552 × 10121
 
0.1%
1.604002679 × 10121
 
0.1%
1.604002681 × 10121
 
0.1%
1.604521418 × 10121
 
0.1%
1.604606903 × 10121
 
0.1%
1.604813811 × 10121
 
0.1%
1.604813817 × 10121
 
0.1%
ValueCountFrequency (%)
1.615225033 × 10121
0.1%
1.614616873 × 10121
0.1%
1.614538558 × 10121
0.1%
1.6141248 × 10121
0.1%
1.61385007 × 10121
0.1%
1.613345597 × 10121
0.1%
1.613235569 × 10121
0.1%
1.613235566 × 10121
0.1%
1.613088 × 10122
0.2%
1.613075216 × 10121
0.1%

purchasedItemCount
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)7.9%
Missing484
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean14.75748
Minimum0
Maximum689
Zeros15
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:16.151911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile88.1
Maximum689
Range689
Interquartile range (IQR)4

Descriptive statistics

Standard deviation61.13424
Coefficient of variation (CV)4.1425934
Kurtosis62.320237
Mean14.75748
Median Absolute Deviation (MAD)1
Skewness7.170786
Sum9371
Variance3737.3954
MonotonicityNot monotonic
2023-07-19T13:08:16.301568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 245
21.9%
2 113
 
10.1%
5 90
 
8.0%
4 67
 
6.0%
3 23
 
2.1%
0 15
 
1.3%
10 12
 
1.1%
9 10
 
0.9%
6 7
 
0.6%
11 5
 
0.4%
Other values (40) 48
 
4.3%
(Missing) 484
43.3%
ValueCountFrequency (%)
0 15
 
1.3%
1 245
21.9%
2 113
10.1%
3 23
 
2.1%
4 67
 
6.0%
5 90
 
8.0%
6 7
 
0.6%
7 3
 
0.3%
8 2
 
0.2%
9 10
 
0.9%
ValueCountFrequency (%)
689 1
0.1%
670 1
0.1%
599 1
0.1%
348 1
0.1%
341 1
0.1%
335 1
0.1%
309 1
0.1%
303 1
0.1%
240 1
0.1%
229 1
0.1%

rewardsReceiptItemList
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing440
Missing (%)39.3%
Memory size8.9 KiB
Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
FINISHED
518 
SUBMITTED
434 
REJECTED
71 
PENDING
 
50
FLAGGED
 
46

Length

Max length9
Median length8
Mean length8.3020554
Min length7

Characters and Unicode

Total characters9290
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFINISHED
2nd rowFINISHED
3rd rowREJECTED
4th rowFINISHED
5th rowFINISHED

Common Values

ValueCountFrequency (%)
FINISHED 518
46.3%
SUBMITTED 434
38.8%
REJECTED 71
 
6.3%
PENDING 50
 
4.5%
FLAGGED 46
 
4.1%

Length

2023-07-19T13:08:16.452662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T13:08:16.598385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
finished 518
46.3%
submitted 434
38.8%
rejected 71
 
6.3%
pending 50
 
4.5%
flagged 46
 
4.1%

Most occurring characters

ValueCountFrequency (%)
I 1520
16.4%
E 1261
13.6%
D 1119
12.0%
S 952
10.2%
T 939
10.1%
N 618
6.7%
F 564
 
6.1%
H 518
 
5.6%
M 434
 
4.7%
B 434
 
4.7%
Other values (8) 931
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9290
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1520
16.4%
E 1261
13.6%
D 1119
12.0%
S 952
10.2%
T 939
10.1%
N 618
6.7%
F 564
 
6.1%
H 518
 
5.6%
M 434
 
4.7%
B 434
 
4.7%
Other values (8) 931
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9290
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1520
16.4%
E 1261
13.6%
D 1119
12.0%
S 952
10.2%
T 939
10.1%
N 618
6.7%
F 564
 
6.1%
H 518
 
5.6%
M 434
 
4.7%
B 434
 
4.7%
Other values (8) 931
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 1520
16.4%
E 1261
13.6%
D 1119
12.0%
S 952
10.2%
T 939
10.1%
N 618
6.7%
F 564
 
6.1%
H 518
 
5.6%
M 434
 
4.7%
B 434
 
4.7%
Other values (8) 931
10.0%

totalSpent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct94
Distinct (%)13.7%
Missing435
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean77.796857
Minimum0
Maximum4721.95
Zeros15
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-07-19T13:08:16.736075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median18.2
Q334.96
95-th percentile290
Maximum4721.95
Range4721.95
Interquartile range (IQR)33.96

Descriptive statistics

Standard deviation347.11035
Coefficient of variation (CV)4.4617529
Kurtosis122.95658
Mean77.796857
Median Absolute Deviation (MAD)16.76
Skewness10.203611
Sum53213.05
Variance120485.59
MonotonicityNot monotonic
2023-07-19T13:08:16.964365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 172
 
15.4%
10 54
 
4.8%
28.57 50
 
4.5%
34.96 44
 
3.9%
49.95 43
 
3.8%
25 28
 
2.5%
84 22
 
2.0%
4.66 15
 
1.3%
0 15
 
1.3%
9.99 14
 
1.3%
Other values (84) 227
20.3%
(Missing) 435
38.9%
ValueCountFrequency (%)
0 15
 
1.3%
0.16 1
 
0.1%
0.99 7
 
0.6%
1 172
15.4%
2 4
 
0.4%
2.23 7
 
0.6%
2.29 1
 
0.1%
2.99 1
 
0.1%
3 9
 
0.8%
3.09 5
 
0.4%
ValueCountFrequency (%)
4721.95 1
0.1%
4566.17 1
0.1%
4368.8 1
0.1%
2084.82 1
0.1%
1198.68 1
0.1%
1183.1 1
0.1%
1177.84 1
0.1%
1107.82 1
0.1%
1083.24 1
0.1%
1043.18 1
0.1%

userId
Categorical

Distinct258
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
5fc961c3b8cfca11a077dd33
436 
59c124bae4b0299e55b0f330
58 
54943462e4b07e684157a532
 
50
5fa41775898c7a11a6bcef3e
 
21
5ff5d15aeb7c7d12096d91a2
 
20
Other values (253)
534 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters26856
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)14.9%

Sample

1st row5ff1e1eacfcf6c399c274ae6
2nd row5ff1e194b6a9d73a3a9f1052
3rd row5ff1e1f1cfcf6c399c274b0b
4th row5ff1e1eacfcf6c399c274ae6
5th row5ff1e194b6a9d73a3a9f1052

Common Values

ValueCountFrequency (%)
5fc961c3b8cfca11a077dd33 436
39.0%
59c124bae4b0299e55b0f330 58
 
5.2%
54943462e4b07e684157a532 50
 
4.5%
5fa41775898c7a11a6bcef3e 21
 
1.9%
5ff5d15aeb7c7d12096d91a2 20
 
1.8%
600fb1ac73c60b12049027bb 16
 
1.4%
5ff1e194b6a9d73a3a9f1052 14
 
1.3%
5ff47392c3d63511e2a47881 10
 
0.9%
600987d77d983a11f63cfa92 10
 
0.9%
5a43c08fe4b014fd6b6a0612 9
 
0.8%
Other values (248) 475
42.4%

Length

2023-07-19T13:08:17.151548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5fc961c3b8cfca11a077dd33 436
39.0%
59c124bae4b0299e55b0f330 58
 
5.2%
54943462e4b07e684157a532 50
 
4.5%
5fa41775898c7a11a6bcef3e 21
 
1.9%
5ff5d15aeb7c7d12096d91a2 20
 
1.8%
600fb1ac73c60b12049027bb 16
 
1.4%
5ff1e194b6a9d73a3a9f1052 14
 
1.3%
5ff47392c3d63511e2a47881 10
 
0.9%
600987d77d983a11f63cfa92 10
 
0.9%
5a43c08fe4b014fd6b6a0612 9
 
0.8%
Other values (248) 475
42.4%

Most occurring characters

ValueCountFrequency (%)
1 2923
10.9%
c 2547
 
9.5%
3 2412
 
9.0%
f 1957
 
7.3%
0 1795
 
6.7%
a 1695
 
6.3%
7 1674
 
6.2%
5 1596
 
5.9%
d 1532
 
5.7%
b 1523
 
5.7%
Other values (6) 7202
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16734
62.3%
Lowercase Letter 10122
37.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2923
17.5%
3 2412
14.4%
0 1795
10.7%
7 1674
10.0%
5 1596
9.5%
6 1491
8.9%
9 1450
8.7%
8 1264
7.6%
4 1181
7.1%
2 948
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
c 2547
25.2%
f 1957
19.3%
a 1695
16.7%
d 1532
15.1%
b 1523
15.0%
e 868
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 16734
62.3%
Latin 10122
37.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2923
17.5%
3 2412
14.4%
0 1795
10.7%
7 1674
10.0%
5 1596
9.5%
6 1491
8.9%
9 1450
8.7%
8 1264
7.6%
4 1181
7.1%
2 948
 
5.7%
Latin
ValueCountFrequency (%)
c 2547
25.2%
f 1957
19.3%
a 1695
16.7%
d 1532
15.1%
b 1523
15.0%
e 868
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2923
10.9%
c 2547
 
9.5%
3 2412
 
9.0%
f 1957
 
7.3%
0 1795
 
6.7%
a 1695
 
6.3%
7 1674
 
6.2%
5 1596
 
5.9%
d 1532
 
5.7%
b 1523
 
5.7%
Other values (6) 7202
26.8%

Interactions

2023-07-19T13:08:07.268241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.096103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:45.278237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:47.760068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:50.574775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:52.898926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.141212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:57.422960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:02.623168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:04.816689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:07.580316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.233442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:45.451216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:47.899254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:50.721138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:53.035960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.273042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:57.855525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:02.764450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:04.946474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:07.958947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.374113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:45.607795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:48.031200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:50.860157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:53.168151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.393485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:58.283710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:02.893548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:05.077986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:08.414819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.511852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:45.760784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:48.152834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:50.995718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:53.292175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.513097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:58.696415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:03.019950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:05.200182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:08.817643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.666369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:45.898476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:48.298137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:51.111881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:53.422946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.631248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:59.091082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:03.139002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:05.322606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:09.281117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.795112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:46.062552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:48.439199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:51.227530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:53.554653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.759627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:59.499147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:03.258704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:05.448586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:09.662638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:43.923075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:46.214159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:48.604055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:51.385170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:53.688085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:55.883914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:59.908931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:03.378607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:05.577791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:10.644897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:44.533844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:46.955779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:49.684654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:52.025349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:54.351544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:56.675080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:01.015680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:04.037988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:06.418822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:11.186101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:44.680163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:47.090550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:49.833948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:52.155743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:54.489965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:56.804401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:01.437880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:04.164957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:06.550720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:11.533627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:44.812931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:47.220552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:49.969555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:52.296198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:54.623071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:07:56.930030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:01.858499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:04.298529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T13:08:06.698516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-19T13:08:17.315737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
bonusPointsEarnedcreateDatedateScannedfinishedDatemodifyDatepointsAwardedDatepointsEarnedpurchaseDatepurchasedItemCounttotalSpentbonusPointsEarnedReasonrewardsReceiptStatus
bonusPointsEarned1.000-0.107-0.107-0.031-0.086-0.0660.1930.0120.2430.1200.9960.182
createDate-0.1071.0001.0000.9930.9930.991-0.1340.551-0.0130.1950.3050.397
dateScanned-0.1071.0001.0000.9930.9930.991-0.1340.551-0.0130.1950.3050.397
finishedDate-0.0310.9930.9931.0001.0001.0000.0370.9200.0420.1310.0810.747
modifyDate-0.0860.9930.9931.0001.0000.974-0.1140.5290.0190.1820.2810.431
pointsAwardedDate-0.0660.9910.9911.0000.9741.0000.0030.9150.0120.1270.0550.701
pointsEarned0.193-0.134-0.1340.037-0.1140.0031.0000.1340.067-0.0280.8510.560
purchaseDate0.0120.5510.5510.9200.5290.9150.1341.000-0.009-0.0060.6680.475
purchasedItemCount0.243-0.013-0.0130.0420.0190.0120.067-0.0091.0000.6440.1190.077
totalSpent0.1200.1950.1950.1310.1820.127-0.028-0.0060.6441.0000.3060.772
bonusPointsEarnedReason0.9960.3050.3050.0810.2810.0550.8510.6680.1190.3061.0000.433
rewardsReceiptStatus0.1820.3970.3970.7470.4310.7010.5600.4750.0770.7720.4331.000

Missing values

2023-07-19T13:08:12.283314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-19T13:08:12.545777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-19T13:08:12.765867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_idbonusPointsEarnedbonusPointsEarnedReasoncreateDatedateScannedfinishedDatemodifyDatepointsAwardedDatepointsEarnedpurchaseDatepurchasedItemCountrewardsReceiptItemListrewardsReceiptStatustotalSpentuserId
05ff1e1eb0a720f0523000575500.0Receipt number 2 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)160968753100016096875310001.609688e+1216096875360001.609688e+12500.01.609632e+125.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '26.00', 'itemPrice': '26.00', 'needsFetchReview': False, 'partnerItemId': '1', 'preventTargetGapPoints': True, 'quantityPurchased': 5, 'userFlaggedBarcode': '4011', 'userFlaggedNewItem': True, 'userFlaggedPrice': '26.00', 'userFlaggedQuantity': 5, '_id': '5ff1e1eb0a720f0523000575'}]FINISHED26.005ff1e1eacfcf6c399c274ae6
15ff1e1bb0a720f052300056b150.0Receipt number 5 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)160968748300016096874830001.609687e+1216096874880001.609687e+12150.01.609601e+122.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '1', 'itemPrice': '1', 'partnerItemId': '1', 'quantityPurchased': 1, '_id': '5ff1e1bb0a720f052300056b'}, {'barcode': '028400642255', 'description': 'DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ', 'finalPrice': '10.00', 'itemPrice': '10.00', 'needsFetchReview': True, 'needsFetchReviewReason': 'USER_FLAGGED', 'partnerItemId': '2', 'pointsNotAwardedReason': 'Action not allowed for user and CPG', 'pointsPayerId': '5332f5fbe4b03c9a25efd0ba', 'preventTargetGapPoints': True, 'quantityPurchased': 1, 'rewardsGroup': 'DORITOS SPICY SWEET CHILI SINGLE SERVE', 'rewardsProductPartnerId': '5332f5fbe4b03c9a25efd0ba', 'userFlaggedBarcode': '028400642255', 'userFlaggedDescription': 'DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ', 'userFlaggedNewItem': True, 'userFlaggedPrice': '10.00', 'userFlaggedQuantity': 1, '_id': '5ff1e1bb0a720f052300056b'}]FINISHED11.005ff1e194b6a9d73a3a9f1052
25ff1e1f10a720f052300057a5.0All-receipts receipt bonus16096875370001609687537000NaN1609687542000NaN51.609632e+121.0[{'needsFetchReview': False, 'partnerItemId': '1', 'preventTargetGapPoints': True, 'userFlaggedBarcode': '4011', 'userFlaggedNewItem': True, 'userFlaggedPrice': '26.00', 'userFlaggedQuantity': 3, '_id': '5ff1e1f10a720f052300057a'}]REJECTED10.005ff1e1f1cfcf6c399c274b0b
35ff1e1ee0a7214ada100056f5.0All-receipts receipt bonus160968753400016096875340001.609688e+1216096875390001.609688e+125.01.609632e+124.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '28.00', 'itemPrice': '28.00', 'needsFetchReview': False, 'partnerItemId': '1', 'preventTargetGapPoints': True, 'quantityPurchased': 4, 'userFlaggedBarcode': '4011', 'userFlaggedNewItem': True, 'userFlaggedPrice': '28.00', 'userFlaggedQuantity': 4, '_id': '5ff1e1ee0a7214ada100056f'}]FINISHED28.005ff1e1eacfcf6c399c274ae6
45ff1e1d20a7214ada10005615.0All-receipts receipt bonus160968750600016096875060001.609688e+1216096875110001.609688e+125.01.609601e+122.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '1', 'itemPrice': '1', 'partnerItemId': '1', 'quantityPurchased': 1, '_id': '5ff1e1d20a7214ada1000561'}, {'barcode': '1234', 'finalPrice': '2.56', 'itemPrice': '2.56', 'needsFetchReview': True, 'needsFetchReviewReason': 'USER_FLAGGED', 'partnerItemId': '2', 'preventTargetGapPoints': True, 'quantityPurchased': 3, 'userFlaggedBarcode': '1234', 'userFlaggedDescription': '', 'userFlaggedNewItem': True, 'userFlaggedPrice': '2.56', 'userFlaggedQuantity': 3, '_id': '5ff1e1d20a7214ada1000561'}]FINISHED1.005ff1e194b6a9d73a3a9f1052
55ff1e1e40a7214ada1000566750.0Receipt number 1 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)160968752400016096875240001.609688e+1216096875300001.609688e+12750.01.609601e+121.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '3.25', 'itemPrice': '3.25', 'needsFetchReview': False, 'originalMetaBriteBarcode': '028400642255', 'originalMetaBriteDescription': 'DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ', 'partnerItemId': '1', 'pointsNotAwardedReason': 'Action not allowed for user and CPG', 'pointsPayerId': '5332f5fbe4b03c9a25efd0ba', 'preventTargetGapPoints': True, 'quantityPurchased': 1, 'rewardsGroup': 'DORITOS SPICY SWEET CHILI SINGLE SERVE', 'rewardsProductPartnerId': '5332f5fbe4b03c9a25efd0ba', 'userFlaggedBarcode': '4011', '_id': '5ff1e1e40a7214ada1000566'}]FINISHED3.255ff1e1e4cfcf6c399c274ac3
65ff1e1cd0a720f052300056f5.0All-receipts receipt bonus160968750100016096875010001.609688e+1216096875020001.609688e+125.01.609688e+121.0[{'brandCode': 'MISSION', 'competitorRewardsGroup': 'TACO BELL TACO SHELLS', 'description': 'MSSN TORTLLA', 'discountedItemPrice': '2.23', 'finalPrice': '2.23', 'itemPrice': '2.23', 'originalReceiptItemText': 'MSSN TORTLLA', 'partnerItemId': '1009', 'quantityPurchased': 1, '_id': '5ff1e1cd0a720f052300056f'}]FINISHED2.235ff1e194b6a9d73a3a9f1052
75ff1e1a40a720f0523000569500.0Receipt number 2 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)160968746000016096874600001.609687e+1216096874610001.609687e+12500.01.609027e+121.0[{'barcode': '046000832517', 'brandCode': 'BRAND', 'description': 'Old El Paso Mild Chopped Green Chiles, 4.5 Oz', 'finalPrice': '10.00', 'itemPrice': '10.00', 'partnerItemId': '0', 'pointsNotAwardedReason': 'Action not allowed for user and CPG', 'pointsPayerId': '5332f5f3e4b03c9a25efd0ae', 'quantityPurchased': 1, 'rewardsGroup': 'OLD EL PASO BEANS & PEPPERS', 'rewardsProductPartnerId': '5332f5f3e4b03c9a25efd0ae', '_id': '5ff1e1a40a720f0523000569'}]FINISHED10.005ff1e194b6a9d73a3a9f1052
85ff1e1ed0a7214ada100056e5.0All-receipts receipt bonus160968753300016096875330001.609688e+1216096875380001.609688e+125.01.609632e+125.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '20.00', 'itemPrice': '20.00', 'needsFetchReview': False, 'partnerItemId': '1', 'preventTargetGapPoints': True, 'quantityPurchased': 5, 'userFlaggedBarcode': '4011', 'userFlaggedNewItem': True, 'userFlaggedPrice': '20.00', 'userFlaggedQuantity': 5, '_id': '5ff1e1ed0a7214ada100056e'}]FINISHED20.005ff1e1eacfcf6c399c274ae6
95ff1e1eb0a7214ada100056b250.0Receipt number 3 completed, bonus point schedule DEFAULT (5cefdcacf3693e0b50e83a36)160968753100016096875310001.609688e+1216096875360001.609688e+12250.01.609632e+123.0[{'barcode': '4011', 'description': 'ITEM NOT FOUND', 'finalPrice': '20.00', 'itemPrice': '20.00', 'needsFetchReview': False, 'partnerItemId': '1', 'preventTargetGapPoints': True, 'quantityPurchased': 3, 'userFlaggedBarcode': '4011', 'userFlaggedNewItem': True, 'userFlaggedPrice': '20.00', 'userFlaggedQuantity': 3, '_id': '5ff1e1eb0a7214ada100056b'}]FINISHED20.005ff1e1eacfcf6c399c274ae6
_idbonusPointsEarnedbonusPointsEarnedReasoncreateDatedateScannedfinishedDatemodifyDatepointsAwardedDatepointsEarnedpurchaseDatepurchasedItemCountrewardsReceiptItemListrewardsReceiptStatustotalSpentuserId
1109603c4a0e0a720fde10000380NaNNaN16145638544941614563854494NaN1614563854494NaNNaNNaNNaNNaNSUBMITTEDNaN5fc961c3b8cfca11a077dd33
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